Machine Vision Tech Revenue will Reach $36 Billion
More organizations are exploring machine vision use cases. Machine vision combines hardware and software to provide operational guidance to devices in the execution of their functions based on the capture and processing of images.
Machine vision is rapidly becoming an enabling technology for emerging automation requirements in automotive, healthcare, manufacturing, retail, smart buildings, smart cities, transportation, and logistics applications.
The total revenue of machine vision technology in the seven major global markets is expected to reach $36 billion by 2027 — that’s up from $21.4 billion in 2022. This growth translates to a CAGR of 11 percent, according to the latest market study by ABI Research.
Machine Vision Market Development
Traditionally, machine vision was primarily focused on surveillance and security, asset monitoring, and defect inspection. These mature applications continue to drive the main bulk of total camera shipments within the enterprise market.
However, the industry is going through the next phase of evolution. The COVID-19 pandemic and the desire for digital business growth have led to the emergence of new use cases — such as occupancy detection, crowd monitoring, predictive maintenance, high precision automated inspection, automated picking, and sorting systems in warehouses.
“These innovative use cases are expected to drive future growth of the industry. A key enabler of these innovation use cases is Machine Learning (ML), particularly Deep Learning (DL) technology in machine vision,” said Lian Jye Su, a principal analyst at ABI Research.
Most technology suppliers offer DL-based solutions that are flexible, scalable, and highly efficient. At the same time, enterprises are waking up to the benefits of DL-based machine vision.
When combined with factors such as decreasing component and engineering cost, increasing ease of integration with third-party solutions, growing open-source software and toolkits, the barrier to adopting an effective machine vision solution has lowered significantly for many enterprises.
Moving forward, distributed computing will become the central theme in the implementation of ML in machine vision. IT platform vendors have been actively launching processors that can run ML models on cameras directly or on gateways and on-premise servers.
Instead of having ML models running in the cloud, these vendors have developed a suite of solutions ranging from ML processors to ML development environment and embedded security enhancement to ensure timely development and deployment of ML models and smooth integration into existing workflows.
Furthermore, this IT domain is expected to become more competitive with the emergence of innovative startups focusing on machine vision at the edge.
Currently, hardware revenue is the main component of the revenue at around 89 percent. However, the share of software and services is expected to grow over time, growing from 11 percent to 16 percent.
Outlook for Machine Vision Applications Growth
With the emergence of DL-based machine vision, more ML solution providers are likely going to build their revenue models around the development, deployment, and maintenance of vertical-specific DL-based machine vision models.
“Instead of fully relying on internal expertise, enterprises can partner with companies to develop targeted solutions together. Such partnerships are critical in reducing the complexity in building and maintaining custom ML models, accelerating time-to-market while maximizing Return On Investment (ROI),” concluded Su.
That said, I expect more enterprise CIOs and CTOs could explore additional applications that combine machine vision with other emerging technologies. The expanding application possibilities are limited only by IT developer imagination and creativity.
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Published at Fri, 25 Feb 2022 13:03:00 +0000
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